在Web2.0時代，各種網路服務興盛，人與他人、服務主機，甚至主機與主機間，開始頻仍地進行訊息交換，資訊爆炸已經不是新名詞，但是資訊量從網路時代開始就毫不停歇地增長，隨著各種應用的廣泛發展，人們需要學習從大量的資訊海中過濾重要的訊息，服務提供者對於使用者活動狀態的追蹤也有迫切的需求。本論文以線上學習平台中的測驗功能做為出發點，探討對於使用者情境的偵測。
在學習平台中，使用者分為教師與學生等角色，學生在每次學習活動告一段落時，接受數位化測驗取得成績，教師身份需負責監督學生表現，鼓勵表現優良的學生，對於進度不如預期的學生則予以協助，但是在一對多的教學方式裡，一個教師要同時面對眾多的學生，成績結果只是一堆未處理的數字，即使教師引用數學工具協助分析，要對不同學生一一做出反應仍然是相當大的負擔。本研究旨在建置結合情境感知與自動通知的測驗系統，發揮數位化測驗優勢－即時性、資料庫以及強大的運算能力，主動監測學生的成績及學習狀況，由推論器計算各個學生學習曲線的線性迴歸方程式，回顧過去的成績資料，將分析化為學習情境，對於各種學習情境中的學生給予適當的回饋，或於彙整後通知教師。除了分析過去的資訊外，將迴歸方程式修改成適合學習曲線特性的預測方程式，用來推導出學生的個人化學習標的，當超過預期標的時，給予鼓勵，而低於標的時，則引發補救措施。這些事件可由自動通知系統的事件管理員來進行排程，依照適當時機觸發，達到多樣化、個人化、自動化的目的。

In the age of Web2.0, various network services became critical. Exchange of messages between entities in the network is so frequent that information explosion is quite common nowadays. Volume of Information passed is growing up rapidly. With the wide development of web applications, people need to learn how to filter the important messages; service providers have urgent need to trace the ever changing role of users. This research studies the detections of the user interaction scenario, based on the result from the test function in the on-line learning platform.
The learning platform users are divided into two groups, teachers and students, based on their roles. Usually students sit for an on-line examination at the end of each learning activity. The teachers are in charge of helping students with their presentations, encouraging those with good grades, and helping the weaker ones to reach their potential. But in the one-to-many teaching method, a teacher needs to face many students and the resultant grade of an examination becomes a heap of fuzzy and difficult to comprehend numbers. Even though some mathematical tools can help the teachers analyze the data, it is still very difficult to provide appropriate response to each student. The purpose of this research motives building an examination system which combines context-awareness and auto-notification, and bring the advantages of digital examination. An inference engine is used to calculate linear regression of learning curve for each student, then review the old data, and transfer the analysis into the learning context. Then the feedback is given to the students under the various learning context or the teacher will get notification after it compile the analysis. Besides analyzing the past data, the linear regression result will be adjusted to fit the characteristics of learning curve and infer the personal goal of the student. If result is better than expected goal, students should be encouraged. On the other hand, the remediable actions will be administered. Those events can be scheduled by the manager of auto-notification system, published in the appropriate time, and achieve the goal of variety, personalization, and automation.

Chap 1 緒論..........................................8
1.1 研究動機..........................................8
1.2 研究目標..........................................9
1.3 內容編排........................................10
Chap 2 背景知識與相關研究.............11
2.1 背景知識.........................................11
2.1.1 學習曲線......................................11
2.1.2 情境感知......................................12
2.1.3 迴歸分析......................................13
2.1.4 Linux Cron ..................................14
2.2 相關研究探討..................................14
2.2.1 通知系統.......................................14
2.2.2 學習平台與歷程分析...................15
Chap 3 設計與實作...............................17
3.1 開發環境...........................................17
3.2 自動通知系統架構...........................17
3.2.1 監控單元........................................17
3.2.2 推論器............................................18
3.2.3 通知管理員....................................18
3.3 情境感知分析...................................18
3.3.1 學習曲線正規化............................19
3.3.2 求出迴歸線....................................21
3.3.3 誤差分析........................................21
3.3.4 學習曲線預測................................23
Chap 4 系統運作驗證...........................25
4.1 系統運作...........................................25
4.2 數據驗證...........................................31
4.2.1 極端值範圍的改善........................31
4.2.2 誤差均方性質................................31
Chap 5 結論與未來展望........................33
5.1 結論...................................................33
5.2 未來展望...........................................33
參考文獻..................................................34

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